捷联式惯导系统动态误差特性研究

根据载体运动状态下捷联惯性导航系统 (SINS)的误差方程时变的特点 ,推导出捷联惯性导航系统动态误差模型 ,并对其在几种动态环境下的误差特性进行了仿真研究 ,结果表明 :与静态误差特性相比 ,捷联式惯导系统的动态误差特性发生了较大的变化。     

Initial alignment of compass based on genetic algorithm-particle swarm optimization

The rapidity and accuracy of the initial alignment influence the performance of the strapdown inertial navigation system (SINS), compass alignment is one of the most important methods for initial alignment. The selection of the parameters of the compass alignment loop directly affects the result of alignment. Nevertheless, the optimal parameters of the compass loop of different SINS are also different. Traditionally, the alignment parameters are determined by experience and trial-and-error, thus it cannot ensure that the parameters are optimal. In this paper, the Genetic Algorithm-Particle Swarm Optimization (GA-PSO) algorithm is proposed to optimize the compass alignment parameters so as to improve the performance of the initial alignment of strapdown gyrocompass. The experiment results showed that the GA-PSO algorithm can find out the optimal parameters of the compass alignment circuit quickly and accurately and proved the effectiveness of the proposed method.     

An INS/GNSS integrated navigation in GNSS denied environment using recurrent neural network

In view of the failure of GNSS signals, this paper proposes an INS/GNSS integrated navigation method based on the recurrent neural network (RNN). This proposed method utilizes the calculation principle of INS and the memory function of the RNN to estimate the errors of the INS, thereby obtaining a continuous, reliable and high-precision navigation solution. The performance of the proposed method is firstly demonstrated using an INS/GNSS simulation environment. Subsequently, an experimental test on boat is also conducted to validate the performance of the method. The results show a promising application prospect for RNN in the field of positioning for INS/GNSS integrated navigation in the absence of GNSS signal, as it outperforms extreme learning machine (ELM) and EKF by approximately 30% and 60%, respectively.